Question answering system-based generation of distractors using machine learning

ABSTRACT

Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.

BACKGROUND

The present invention relates generally to the field of computerizedtest item generation, and more particularly to automatically generatingdistractors for text-based multiple-choice test items.

A multiple-choice test is a form of assessment in which respondents areasked to select the best possible answer or answers out of the choicesfrom a list. Among the possible choices are, typically, the correctanswer and a number of incorrect answers, called distractors.

Question Answering (QA) is a computer science discipline within thefields of information retrieval and natural language processing which isconcerned with building systems that automatically answer questionsposed in a natural language. In response to a question, or query, atypical QA system may return a list of answers, ranked by degree ofconfidence in the correctness of each answer.

SUMMARY

Embodiments of the present invention disclose a method, computer programproduct, and system for generating distractors for text-based multiplechoice test (MCT) items. An MCT item stem is received. The stem istransmitted to a question answering (QA) system and a plurality ofcandidate answers related to the stem is received from the QA system.Incorrect answers in the plurality of candidate answers are identified.Textual features are extracted from the stem. A set of semantic criteriaassociated with the extracted textual features is generated. Based onthe semantic criteria, a subset of the incorrect candidate answers isselected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a distractor generationenvironment, in accordance with an embodiment of the present invention.

FIG. 2 is a flowchart depicting operational steps of a distractorgenerator, in accordance with an embodiment of the present invention.

FIG. 3 is a functional block diagram illustrating a data processingenvironment, in accordance with an embodiment of the present invention.

FIG. 4 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 5 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Embodiments of the present invention are directed to methods forautomatically generating, by a computer, distractors for text-basedmultiple-choice test items.

A multiple-choice test (MCT) item consists of a stem and a set ofalternatives. The stem is the beginning part of the item that presentsthe item as a problem to be solved, a question to be answered, or anincomplete statement to be completed. A stem may also include otherrelevant information. The alternatives are the possible answers that therespondent can choose from, with the correct answer called the key andthe incorrect answers called distractors. For example, an MCT item mayconsist of a stem “What is the capital of India?”, and alternatives “NewDelhi”, “Shimla”, “Kolkata”, and “Mumbai”. In this instance, the key is“New Delhi”, while “Shimla”, “Kolkata”, and “Mumbai” are distractors.

Efficient distractors are viable alternatives for an MCT item which,despite being incorrect, are nonetheless plausible. For example, for thestem just mentioned, “What is the capital of India?”, the distractors“Shimla”, “Kolkata”, and “Mumbai” are populous cities in India, whichare capitals of a state.

Machine learning is a subfield of computer science and statistics thatexplores the construction and study of algorithms that can learn fromdata. Such algorithms operate by building a model based on inputs andusing the model to make predictions or decisions, rather than followingonly explicitly programmed instructions. Classification is a machinelearning task in which inputs are assigned to, or labeled as belongingto, two or more classes. Applications of classification include spamfiltering and optical character recognition.

In supervised machine learning, a classification function may beinferred, or trained, from a set of labeled training data. The trainingdata consists of training examples, typically pairs of input objects anddesired output objects, for example class labels. During training, theparameters of the model are adjusted, usually iteratively, so thatinputs are assigned to one or more of the classes to some degree ofaccuracy, based on a predefined metric. The inferred classificationfunction can then be used to categorize new examples. One example of amachine learning classification technique is multi-label classification(MLC), in which multiple class labels, or categories, are assignedsimultaneously to a single instance of an object. In multi-label textclassification, for instance, a document or text passage is associatedwith a set of keywords or topics.

Feature extraction is often applied before MLC, in order to reduce andrefine the set of relevant labels. In machine learning, featureextraction starts from an initial set of data, for example a textdocument, and builds derived values, or features, intended to beinformative, non-redundant, and which facilitate subsequent learning andgeneralization steps. The extracted features are expected to contain therelevant information from the input data, so that a desired task may beperformed by using the reduced representation rather than the completeinitial data.

Textual features extracted from a text document, or a string such as anMCT item stem, may include nouns and adjectives present in the text andmay exclude common words such as “the, is, that, a, on”, etc. Ingeneral, features extracted from a text document may include terms inthe document and concepts semantically related to terms in the document.Common methods of textual feature extraction include Principal ComponentAnalysis and Latent Semantic Analysis.

In machine learning, a hypothesis is a statement relating descriptiveattributes of a concept to values those attributes may assume ininstances of the concept. A simple example is an attribute-value pair,such as “attribute A has value V”. Hypothesis evaluation is the testingof a hypothesis against the set of categorized training instances in themachine learning model. By answering the question, “What patterns matchthe data?”, machine learning algorithms may automate the generation andevaluation of hypotheses.

FIG. 1 is a functional block diagram of a distractor generationenvironment 199, in accordance with an embodiment of the currentinvention. Distractor generation environment 199 includes computingdevice 100, which further includes QA system 110 and distractorgenerator (DG) 112. Distractor generation environment 199 includes avariety of data repositories, for example, trained machine learningmodel 122, criteria dictionary 124, rules repository 126, initialdistractor repository 128, relevant criteria repository 130, andhypotheses repository 132. Trained learning model 122, criteriadictionary 124, and the repositories 126, 128, 130, and 132, 124 mayreside, for example, on computer readable storage media 308 (FIG. 3), oron cloud computing node storage system 34 (FIG. 4).

Distractor generation environment 199 illustrates one example of thebasic functional blocks of a distractor generation environment. Othermodels for a distractor generation environment may be available, whichdescribe a distractor generation environment in greater or lessergranularity and with different functional boundaries between functionalblocks.

QA system 110 may be any of a variety of proprietary, open source, orWeb-based QA systems, for example ASK.COM or the START (SynTacticAnalysis using Reversible Transformations) natural language QA system.

DG 112 may include initial distractor generation module 113, featureextraction module 114, relevant criteria generation module 116,hypotheses generation module 118, and distractor selection module 120.

In an exemplary embodiment of the invention, initial distractorgeneration module 113 may query QA system 110, using as input an MCTitem stem. QA system 110 may return a list of possible, or candidate,answers, ranked according to a confidence score, a numerical value whichrepresents a degree of confidence in the correctness of each candidateanswer. Initial distractor generation module 113 may select a number ofthe highest ranked candidate answers. The number of candidate answersselected may be, for example, at least the number of alternativesrequired for an MCT item, and at most a predefined maximum number.Initial distractor generation module 113 may identify and delete correctanswers, if present, from the list of candidate answers to produce aninitial set of candidate distractors. For example, initial distractorgeneration module 113 may determine the degree to which a candidateanswer matches the key using a string similarity measure. DG 112 maystore the selected candidate distractors in initial distractorrepository 128. Table 1 illustrates a possible set of candidate answersto the query “What is the capital of India” from an exemplary QA system.

TABLE 1 CANDIDATE ANSWER COMMENTS 1 New Delhi Correct answer 2 PatnaCapital of Maurya Empire of ancient India 3 Kolkata Capital of BritishIndia until 1911 4 Shimla Summer capital of India during British times 5Hastinapur Capital of ancient Indian kingdoms 6 NCR National CapitalRegion 7 Mumbai Commercial capital of India 8 Delhi State in Indiacontaining New Delhi 9 Gurgaon City within National Capital RegionAfter removing the correct answer, New Delhi, the list of candidatedistractors may comprise candidate answers 2-9 in Table 1.

In an exemplary embodiment of the invention, feature extraction module114 may identify keywords or topics associated with the stem. Forexample, feature extraction module 114 may analyze the grammaticalstructure of the stem to identify words and phrases which are thesubject or object of a verb. This analysis may include tagging the wordsof the stem with parts of speech, such as nouns, verbs, adjectives,adverbs, pronouns, conjunctions, prepositions, interjections, andarticles. This tagging may be accomplished, for example, using a naturallanguage parsing program such as the Stanford Parser, version 3.5.1,available from The Stanford Natural Language Processing Group atStanford University, or other proprietary and/or open source naturallanguage parsers. The identified words and phrases may be analyzed toidentify additional, semantically related keywords and topics in alexical database such as WordNet, which may identify synonyms, or acommonsense knowledge base such as ConceptNet, which may identify termsrelated by meaning. For example, for the stem “What is the capital ofIndia?”, natural language parsing may generate the list of words, What,is, the, capital, of, India. Semantic analysis may then generate, asextracted features, the words, what, is, capital, India, country,location, city.

In an exemplary embodiment of the invention, relevant criteriageneration module 116 operates generally to use the features extractedby feature extraction module 114, along with a trained machine learningmodel 122, applied to a set of criteria defined in criteria dictionary124, to select a set of criteria relevant to the extracted features, andstore the relevant criteria in relevant criteria repository 130, asdescribed in more detail below.

In an embodiment of the invention, criteria dictionary 124 may contain aset of defined criteria. A criterion may be an attribute, category, orfact about an entity or event. Criteria may be domain-specific, forexample in the medical domain, or domain independent, i.e., generic.Criteria may also be question independent or question dependent. Forexample, a criterion may be ‘Is Country’ (question independent) or ‘IsInside % Object %’ (question dependent), where % Object % is aplaceholder, or variable, that stands for the main object of thequestion, which may be determined, for example, through natural languageprocessing (NLP) techniques such as syntactic or semantic parsing. Acriterion may have a list of synonyms. For example, the criterion ‘IsInside % Object %’ may have synonyms such as ‘Is Part of % Object %’ or‘Is Within % Object %’. Notation such as % Object % may be used todifferentiate a question-dependent criterion, where the value of %Object % is derived, for example, from NLP analysis data, from aquestion-independent criterion such as Object, without the % . . . %variable markers. If a criterion contains a variable such as % Object %,the variable may be resolved to a definite value, based, for example, ontyped dependencies or other NLP analysis techniques, before thecriterion is evaluated or applied. Criteria dictionary 124 may begeneric/domain independent, or domain specific. Criteria dictionary 124may contain all independent criteria, or may contain onlyquestion-dependent criteria, or a combination of independent andquestion-dependent criteria. In various embodiments, criteria dictionary124 may be defined and implemented, for example, via a text list,key-value pairs, XML, or a table in a data repository, etc.

Table 2 shows a criteria dictionary 124, in an exemplary embodiment ofthe invention:

TABLE 2 QUESTION-INDEPENDENT QUESTION-DEPENDENT CRITERIA CRITERIA IsCountry Is Inside %Object% Is City Is Part of %Object% Is Capital IsWithin %Object% Is Current Capital Is Electronic Device Is CricketPlaying Country Is Politician Is Scientist Is Researcher

In an embodiment of the invention, trained machine learning model 122may apply MLC to the features extracted by feature extraction module114, using the criteria contained in criteria dictionary 124 as labels,to select as relevant criteria, for example, those criteria chosen aslabels for the extracted features, and may store the subset of relevantcriteria in relevant criteria repository 130. For example, the output oftrained machine learning model 122, applied to the extracted featureswhat, is, capital, India, country, location, city, using the exemplarycriteria dictionary shown above, may be the relevant criteria Is City,Is Current Capital, Is Inside % Object %.

In an exemplary embodiment of the invention, trained machine learningmodel 122 may be trained to apply MLC, for example, using supervisedlearning with manually labeled data, comprising questions such as “Whois the president of the U.S.?”, paired with criteria such as “Is currentpresident”, “Is politician”, or “Citizen of % Object %”.

In an embodiment of the invention, relevant criteria generation module116 may further apply a set of rules, which may reside in rulesrepository 126, to the relevant criteria stored in relevant criteriarepository 130, in order to refine the relevant criteria. For example, arule may be “use only question-independent criteria”. In this case, forthe relevant criteria shown above, the refined set of relevant criteriamay be Is City, Is Current Capital. In certain embodiments, rules may bestatic, or predefined. In other embodiments, rules may be dynamicallygenerated, for example, based on specific MCT items or the domain of theMCT.

In an embodiment of the invention, hypothesis generation module 118 maycombine the refined relevant criteria generated by relevant criteriageneration module 116 with the candidate distractors generated byinitial distractor generation module 113 and stored in initial candidatedistractor repository 128 to generate hypotheses concerning the initialcandidate distractors. For example, for the candidate distractors ofTable 1, a generated set of hypotheses may be

-   -   Patna is city    -   Patna is current capital    -   Kolkata is city    -   Kolkata is current capital    -   Shimla is city    -   Shimla is current capital    -   Hastinapur is city    -   Hastinapur is current capital    -   NCR is city    -   NCR is current capital    -   Mumbai is city    -   Mumbai is current capital    -   Delhi is city    -   Delhi is current capital    -   Gurgaon is city    -   Gurgaon is current capital

The term evidence-based refers to a concept or strategy that is derivedfrom or informed by objective evidence, for example data, academicresearch, or scientific findings. In evidence-based scoring, ahypothesis is evaluated by first collecting evidence supporting orrefuting the hypothesis, and then assigning to the hypothesis aconfidence score, or numerical value representing the degree to whichthe evidence justifies or refutes the hypothesis.

In an embodiment of the invention, distractor selection module 120 mayevaluate each of the hypotheses generated by hypotheses generationmodule 118 to select a set of efficient distractors. For example,distractor selection module 120 may employ evidence-based scoring toselect the efficient distractors. Distractor selection module 120 maycollect evidence in the form of text passages retrieved from a knowledgebase such as Wikidata in response to the hypothesis, entered as a query.In computing a confidence score, distractor selection module 120 mayconsider the degree of match between the retrieved passages'predicate-argument structure and the hypothesis, passage sourcereliability, geospatial location, temporal relationships, taxonomicclassification, lexical and semantic relations the hypothesis is knownto participate in, its popularity or obscurity, its aliases, and so on.For example, distractor selection module 120, employing evidence-basedscoring, might evaluate the generated hypotheses to either True or Falseand assign an evidence score as follows

-   -   Patna is city (True, evidence score 0.75)    -   Patna is current capital (True, evidence score 0.85)    -   Kolkata is city (True, evidence score 0.8)    -   Kolkata is current capital (True, evidence score 0.9)    -   Shimla is city (True, evidence score 0.8)    -   Shimla is current capital (True, evidence score 0.85)    -   Hastinapur is city (True, evidence score 0.7)    -   Hastinapur is current capital (False, evidence score 0.35)    -   NCR is city (False, evidence score 0.2)    -   NCR is current capital (False, evidence score 0.4)    -   Mumbai is city (True, evidence score 0.9)    -   Mumbai is current capital (True, evidence score 0.9)    -   Delhi is city (False, evidence score 0.45)    -   Delhi is current capital (True, evidence score 0.9)    -   Gurgaon is city (True, evidence score 0.85)    -   Gurgaon is current capital (False, evidence score 0.4)        Based on this exemplary output, distractor selection module 120        may select the candidate distractors for which the generated        hypotheses were evaluated as True as efficient distractors:        Patna, Kolkota, Shimla, Mumbai.

Distractor selection module 120 may further reduce the set of efficientdistractors to a predefined number. For example, if for an MCT item onlythree distractors are desired, distractor selection module 120 mayselect the three efficient distractors with the highest averageevidence-based score. In the foregoing example, these may be Kolkota,Shimla, Mumbai.

FIG. 2 is a flowchart depicting various steps used in a DG 112 (FIG. 1),in accordance with an exemplary embodiment of the invention. DG 112(FIG. 1) may receive an MCT item stem, along with its key (step 210).Initial distractor generation module 113 (FIG. 1) may submit the stem toQA system 110 (FIG. 1) as a query and receive from QA system 110(FIG. 1) a list of candidate answers to the query (step 212). Initialdistractor generation module 113 (FIG. 1) may identify any correctanswers, if present, by comparison with the key, and remove them fromthe list of candidate answers (step 214). The remaining candidateanswers may constitute an initial set of candidate distractors. Initialdistractor generation module 113 (FIG. 1) determines if there are enoughcandidate distractors, based on a predetermined threshold (decision step216). If there are not enough candidate distractors (decision step 216,“NO” branch), processing ends. If there are enough candidate distractors(decision step 216, “YES” branch), processing continues. Featureextraction module 114 (FIG. 1) may analyze the stem to extract a set offeatures (step 218). Relevant criteria generation module 116 (FIG. 1)may apply trained machine learning model 122 (FIG. 1) using, forexample, MLC with the extracted features and criteria dictionary 124(FIG. 1) as inputs, to generate an initial set of relevant criteria(step 220). Relevant criteria generation module 116 (FIG. 1) may refinethe relevant criteria using a predefined set of rules (step 222).Hypotheses generation module 118 (FIG. 1) may apply the refined set ofrelevant criteria to the initial set of candidate distractors togenerate hypotheses for each candidate distractor (step 224). Distractorselection module 120 (FIG. 1) may evaluate the hypotheses to select aset of efficient distractors (step 226), which may be reduced to apredetermined number of distractors (step 228).

FIG. 3 depicts a block diagram 300 of components of a computing device100 (FIG. 1), in accordance with an embodiment of the present invention.It should be appreciated that FIG. 3 provides only an illustration ofone implementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

Computing device 100 may include one or more processors 302, one or morecomputer-readable RAMs 304, one or more computer-readable ROMs 306, oneor more computer readable storage media 308, device drivers 312,read/write drive or interface 314, network adapter or interface 316, allinterconnected over a communications fabric 318. Communications fabric318 may be implemented with any architecture designed for passing dataand/or control information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system.

One or more operating systems 310, and one or more application programs328, for example, DG 112 and/or QA system 110 (FIG. 1), are stored onone or more of the computer readable storage media 308 for execution byone or more of the processors 302 via one or more of the respective RAMs304 (which typically include cache memory). In the illustratedembodiment, each of the computer readable storage media 308 may be amagnetic disk storage device of an internal hard drive, CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk, asemiconductor storage device such as RAM, ROM, EPROM, flash memory orany other computer-readable tangible storage device that can store acomputer program and digital information.

Computing device 100 may also include a R/W drive or interface 314 toread from and write to one or more portable computer readable storagemedia 326. Application programs 328 on computing device 100 may bestored on one or more of the portable computer readable storage media326, read via the respective R/W drive or interface 314 and loaded intothe respective computer readable storage media 308.

Computing device 100 may also include a network adapter or interface316, such as a TCP/IP adapter card or wireless communication adapter(such as a 4G wireless communication adapter using OFDMA technology).Application programs 328 on computing device 100 may be downloaded tothe computing device from an external computer or external storagedevice via a network (for example, the Internet, a local area network orother wide area network or wireless network) and network adapter orinterface 316. From the network adapter or interface 316, the programsmay be loaded onto computer readable storage media 308. The network maycomprise copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers and/or edge servers.

Computing device 100 may also include a display screen 320, a keyboardor keypad 322, and a computer mouse or touchpad 324. Device drivers 312interface to display screen 320 for imaging, to keyboard or keypad 322,to computer mouse or touchpad 324, and/or to display screen 320 forpressure sensing of alphanumeric character entry and user selections.The device drivers 312, R/W drive or interface 314 and network adapteror interface 316 may comprise hardware and software (stored on computerreadable storage media 308 and/or ROM 306).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications andsubstitutions can be made without deviating from the scope of thepresent invention. Therefore, the present invention has been disclosedby way of example and not limitation.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 4, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and distractor generation 96.

The foregoing description of various embodiments of the presentinvention has been presented for purposes of illustration anddescription. It is not intended to be exhaustive nor to limit theinvention to the precise form disclosed. Many modifications andvariations are possible. Such modification and variations that may beapparent to a person skilled in the art of the invention are intended tobe included within the scope of the invention as defined by theaccompanying claims.

What is claimed is:
 1. A method for generating distractors fortext-based multiple choice test (MCT) items, the method comprising:receiving, by a first computer from a second computer over a network, inresponse to information entered into a user interface on the secondcomputer, an MCT item stem and key; submitting, by the first computer,the stem to a question answering (QA) system wherein a QA system is acomputer system that, in response to a query, automatically generates alist of candidate answers to the query; in response to submitting thestem to the QA system, receiving, by the first computer, from the QAsystem a plurality of candidate answers; identifying, by the firstcomputer, one or more incorrect candidate answers in the plurality ofcandidate answers; extracting, by the first computer, textual featuresfrom the stem, wherein a textual feature is a term in the stem or aconcept semantically related to a term in the stem; applying, by thefirst computer, a machine learning model to generate a set of semanticcriteria associated with the extracted textual features; selecting, bythe first computer, as distractors one or more of the incorrectcandidate answers, that satisfy the generated semantic criteria;creating, by the first computer, an MCT item that includes the stem, thekey, and the distractors; and transmitting, by the first computer, thecreated MCT item via the network to the second computer.
 2. A method inaccordance to claim 1, wherein the stem is one of: a question to beanswered, a problem to be solved, or an incomplete statement to becompleted.
 3. A method in accordance to claim 1, wherein the MCT itemstem includes a predefined number of alternative choices, and theplurality of candidate answers is at least the predefined number ofalternative choices and less than a predefined maximum number.
 4. Amethod in accordance to claim 1, wherein identifying, by the firstcomputer, incorrect answers comprises: identifying, by the firstcomputer, a correct answer by comparing the plurality of candidateanswers with the key; and deleting, by the first computer, the correctanswer from the plurality of candidate answers.
 5. A method inaccordance to claim 1, wherein generating the set of semantic criteriaassociated with the extracted textual features comprises: applying, bythe first computer, a trained machine learning model to the extractedtextual features, using a predefined set of semantic criteria; andrefining, by the first computer, the set of semantic criteria byapplying a predefined set of rules.
 6. A method in accordance to claim5, wherein applying, by the first computer, a trained machine learningmodel further comprises using, by the first computer, multi-labelclassification.
 7. A method in accordance to claim 1, wherein selectinga subset of the incorrect candidate answers, based on the generatedsemantic criteria, comprises: generating, by the first computer,hypotheses concerning the semantic criteria and the incorrect candidateanswers; collecting, by the first computer, evidence supporting orrefuting each hypothesis; assigning, by the first computer, to eachhypothesis a numerical value representing a degree to which the evidencejustifies or refutes the hypothesis; and selecting, by the firstcomputer, the subset of the incorrect candidate answers based on thenumerical value.